code: def _has_potential_branch_input_alias(branch, inputs):
      """
      Dispatch-trace the branch with inputs and check if
      producing graph has output aliasing the branch input. This is
      bit restrictive as the branch must be traceable.
      """
      try:
          gm = make_fx(branch)(*inputs)
  
      except UnsupportedAliasMutationException:
          # this can happen when nested cond_op is
          # functionalized
          return True
      except Exception as e:
          raise e
  
      def _detect_input_alias(gm):
          input_storages = set()
          for node in gm.graph.nodes:
              # We need to check existence of "val" because we reuse the logic here
              # for map operator, where num_mapped_args is a scalar
              # and doesn't have a "val" meta.
              if node.op == "placeholder" and "val" in node.meta:
                  input_storages.add(
                      StorageWeakRef(node.meta["val"]._typed_storage())
                  )
              if node.op == "output":
  
                  def check_alias(out):
                      if out is not None and "val" in out.meta:
                          out_storage = StorageWeakRef(
                              out.meta["val"]._typed_storage()
                          )
                          return out_storage in input_storages
                      return False
  
                  if any(
                      pytree.tree_leaves(
                          pytree.tree_map(check_alias, node.args)
                      )
                  ):
                      return True
  
          for _, module in gm.named_children():
              if isinstance(
                  module, torch.fx.GraphModule
              ) and _detect_input_alias(module):
                  return True
  
          return False
  
      return _detect_input_alias(gm)
  
imports: from contextlib import contextmanager
  from dataclasses import dataclass
  import torch
  import torch._subclasses.functional_tensor
  import torch.fx.traceback
  import torch.utils._pytree
  from torch._C import DispatchKey
  from torch._C._functorch import _add_batch_dim, get_unwrapped, is_batchedtensor, maybe_get_bdim
  from torch._functorch.utils import exposed_in
  from torch._higher_order_ops.utils import autograd_not_implemented
  from torch._ops import HigherOrderOperator
  from torch._subclasses.fake_tensor import FakeTensorMode
  from torch.fx.experimental.proxy_tensor import disable_proxy_modes_tracing, make_fx, ProxyTorchDispatchMode, track_tensor_tree
  from torch.fx.passes.shape_prop import _extract_tensor_metadata
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._python_dispatch import _get_current_dispatch_mode
label_desc: Use image node_id 9 for calling a global function with example usage: _has_potential_branch_input_alias(branch, inputs) and returns: _detect_input_alias, False, True, True, False, True, unknown